AI/ML Introduction: Episode #8: Other Types of Machine Learning
Machine learning algorithms are used to identify patterns, make decisions and predict outcomes. They are trained using a large amount of data and use statistical methods to discover underlying relationships and trends in the data that can be used to make predictions or recommendations.
Machine learning algorithms are used in applications such as fraud detection, image recognition, credit scoring, web search ranking, recommendation engines, natural language processing and more.
There are four main types of machine learning techniques such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning, which I have explained in my previous blog.
Apart from the above there are also some other types of machine learning techniques that are being used.
In this blog, I will cover the other popularly used machine learning methods such as:
#1: Transfer Learning:
Transfer learning is an important concept in machine learning which allows machines to learn from previously acquired knowledge and apply it to a new task. It has been used in many areas with great success, including robotics, natural language processing, computer vision, and more.
One example of transfer learning is the use of deep neural networks for image classification. Neural networks are trained on large datasets of images containing objects such as cats and dogs. After initial training, the same neural network can be used to classify different objects such as cars or trees. This process can save tremendous amounts of time and resources when compared to training a new model from scratch for each object type.
#2: Deep Learning:
Deep learning is an advanced form of machine learning, which works on the basis of providing machines with access to data and allowing them to find patterns by themselves. By doing so, machines can learn to represent data in multiple levels of detail, abstraction, and complexity. This way, deep learning helps machines quickly solve complex tasks or make decisions without requiring manual programming.
For example, deep learning is used for natural language processing (NLP) and image recognition. In NLP tasks such as sentiment analysis and question-answering systems, machines use deep learning models to better understand human language by taking into account context and meaning. Similarly for image recognition applications such as facial recognition or object detection, deep learning models are used to detect objects within an image by understanding their shape and characteristics.
#3: Self-Supervised Learning;
Self-supervised learning is a type of machine learning in which the machine uses data labeled by itself to learn. It is an unsupervised learning algorithm that leverages unlabeled data to identify patterns and acquire knowledge from it. Self-supervised learning can be used in tasks such as predictive modeling, clustering, pattern recognition and more.
One example application of self-supervised learning is voice detection. By labelling audio data with its own labels, the algorithm can detect different sounds or words within an audio sample, allowing machines to recognize and interact with humans through voice commands. Another example of self-supervised learning is computer vision, where algorithms use unlabelled images to train their models on how to identify and classify objects within the image.
#4: Multiple Instance Learning:
Multiple Instance Learning (MIL) is a form of supervised learning in which each instance or example of data is grouped into “bags” or sets. Each bag may contain multiple instances, and the entire bag is given a label. For example, consider an image dataset containing photos of cats and dogs. Each photo can be considered an individual instance, while the set of all cat photos would comprise a single bag labeled “cat”, and likewise for the dog photos. In other words, MIL differs from standard supervised machine learning because it does not require labels for individual instances — only labels for bags of instances.
For example, in medical diagnosis settings where symptoms are often subjective and hard to define precisely as individual parameters; MIL could provide a way to group related symptoms together as bags and classify them more accurately than by attempting to classify individual symptom occurrences alone.
#5: Inductive Learning
Inductive learning is a type of machine learning technique where the algorithm uses existing data to construct rules and generalize them to new observations. Unlike supervised learning that requires labeled data, inductive learning uses input and output data to learn patterns between them.
For example, an inductive learner can be used for facial recognition in surveillance systems by analyzing faces in video footage or photographs. In this application, the learner builds a model by studying a large number of images containing various people’s faces and then compares it with incoming images to detect whether they are identical or not.
#6: Deductive Learning
Deductive learning is a type of machine learning technique where the machine uses labelled data to form a generalization of this data. In other words, it can be used to create a hypothesis and use this hypothesis to make predictions about unlabelled data.
For example, in NLP, it has been used to learn associative relationships between phrases by determining which phrases are likely to occur together and then making predictions about unseen phrases based on these associations. In recommendation systems, it has been used to recommend products or services based on user behaviour by inferring a user’s preferences from their past purchases or interactions with the system.
#7: Transductive Learning
Transductive learning is a type of machine learning that uses unlabeled data to make predictions about the labels of new data. This method can be used to create more accurate and effective models for tasks such as image classification, object detection, and automated language understanding. Unlike inductive machine learning, transductive learning does not require labeled data in order to make accurate predictions; instead, it makes use of the existing information within the dataset to infer labels for new points.
An example application of transductive learning could be in medical diagnosis. For example, a computer system could be trained with unlabelled medical images that contain no clinical information (e.g., X-rays or MRI scans). The model would then learn how certain shapes and textures correspond to various diseases, allowing it to detect and diagnose diseases without needing any prior label information.
#8: Multi-task learning
Multi-task learning is a type of machine learning in which the algorithm learns from multiple tasks without sharing information between them. Multi-task learning allows machines to learn more effectively by combining multiple tasks, resulting in better overall performance.
For example, in NLP applications, multi-task instance learning can help machines learn language more quickly and accurately by allowing them to learn related tasks simultaneously. For example, a machine might be taught both word meaning and sentence structure at the same time so that it can better interpret language.
#9: Active Learning:
Active learning is an innovative machine learning technique that enables machines to quickly learn from limited data. It works by constructing a number of ‘policies’ based on the data it receives, which can then be used to make predictions on unseen or new data. This technique has been used in many areas such as natural language processing, computer vision, reinforcement learning, and automated planning.
For example, in natural language processing (NLP) tasks such as machine translation and speech recognition, active learning techniques have been developed to enable machines to learn more efficiently from the data they are given. Active learning algorithms have been shown to improve the performance of NLP applications when compared with traditional supervised methods. The same is true for computer vision applications — active learning can be used to reduce training time and improve accuracy.
Conclusion:
As highlighted, there are multitude of techniques, each to tackle a specific flavour of the objective. Some of these techniques are tailored specifically for applications, while some have a more generic methodology which makes them almost universal.
The idea is to know the application and employ just the right approach for it. This would help optimize the model building process and make sure that you get good results in minimum possible time.
Therefore, understanding each of these techniques, their application and utility is essential for any aspiring Machine Learning practitioner. With that in mind, one can easily decide which technique to choose for a given problem with confidence.